{"id":1430,"date":"2023-06-06T21:44:29","date_gmt":"2023-06-06T21:44:29","guid":{"rendered":"https:\/\/dailyai.com\/?p=1430"},"modified":"2023-06-06T22:28:18","modified_gmt":"2023-06-06T22:28:18","slug":"researchers-create-high-performance-ai-model-to-detect-heart-disease-from-ecgs","status":"publish","type":"post","link":"https:\/\/dailyai.com\/sv\/2023\/06\/researchers-create-high-performance-ai-model-to-detect-heart-disease-from-ecgs\/","title":{"rendered":"Forskare skapar h\u00f6gpresterande AI-modell f\u00f6r att uppt\u00e4cka hj\u00e4rtsjukdomar fr\u00e5n EKG"},"content":{"rendered":"<p><b>Researchers at Mount Sinai have used machine learning to develop a novel electrocardiogram (ECG) analysis model called HeartBEiT. <\/b><\/p>\n<p>HeartBEiT was pre-trained on a colossal dataset of 8.5 million ECGs from 2.1 million patients and outperformed classic convolutional neural networks (CNNs).<\/p>\n<p><span style=\"font-weight: 400\">ECGs are ubiquitous heart diagnostic tests, and some 100 million are performed each year in the US alone.<\/span><\/p>\n<p><span style=\"font-weight: 400\">An ECG measures electrical activity from the heart and is highly informative of a huge range of rhythm disorders, abnormal heart activity associated with heart attacks, and other forms of heart disease. The issue with ECGs is many of the subtler indications are tough to interpret, especially for rare heart conditions.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Prior to this study, machine learning (ML) had already been applied to ECG images to automatically flag and classify abnormal results. This increases diagnostic speed and accuracy and reduces the chance of clinical oversight.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Mount Sinai, a New York hospital and research network, trained a vision-based transformer model on 8.5 million ECG scans.<\/span><\/p>\n<p><span style=\"font-weight: 400\"> Notably, the model differs from convolutional neural networks (CNNs), the most common neural network for image classification tasks. <\/span><span style=\"font-weight: 400\">The <\/span><a href=\"https:\/\/www.nature.com\/articles\/s41746-023-00840-9\"><span style=\"font-weight: 400\">study<\/span><\/a><span style=\"font-weight: 400\">, published in Nature, states that the model outperformed other models for identifying various heart conditions.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400\">How HeartBEiT works<\/span><\/h2>\n<p><span style=\"font-weight: 400\">CNNs for image classification involve supervised machine learning, meaning they require large quantities of labeled data, which can be labor-intensive to collect and annotate. Conversely, HeartBEiT uses unsupervised techniques to learn from a vast dataset, providing a foundation for downstream tuning to more specific tasks.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">The research team used the DALL-E model, created by OpenAI. DALL-E learns the relationships between tokens. In this case, parts of the ECG images \u2013 the tokens \u2013 are broadly analogous to words in a sentence.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">The model learns their relationship to each other, which allows it to single out abnormal tokens that indicate cardiac problems.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">HeartBEiT\u2019s predictions were compared against standard CNN architectures for several heart conditions, outperforming them over smaller sample sizes.\u00a0<\/span><\/p>\n<figure id=\"attachment_1431\" aria-describedby=\"caption-attachment-1431\" style=\"width: 647px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-1431\" src=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/41746_2023_840_Fig5_HTML-925x1024.webp\" alt=\"\" width=\"647\" height=\"716\" srcset=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/41746_2023_840_Fig5_HTML-925x1024.webp 925w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/41746_2023_840_Fig5_HTML-271x300.webp 271w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/41746_2023_840_Fig5_HTML-768x850.webp 768w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/41746_2023_840_Fig5_HTML-370x410.webp 370w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/41746_2023_840_Fig5_HTML-800x885.webp 800w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/41746_2023_840_Fig5_HTML-740x819.webp 740w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/41746_2023_840_Fig5_HTML-20x22.webp 20w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/41746_2023_840_Fig5_HTML-43x48.webp 43w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/41746_2023_840_Fig5_HTML.webp 1348w\" sizes=\"auto, (max-width: 647px) 100vw, 647px\" \/><figcaption id=\"caption-attachment-1431\" class=\"wp-caption-text\">HeartBEiT (D) highlights specific abnormal regions more accurately than other models. Source: <a href=\"https:\/\/www.nature.com\/articles\/s41746-023-00840-9\">Nature<\/a>.<\/figcaption><\/figure>\n<p><span style=\"font-weight: 400\">One of the authors, Akhil Vaid, Instructor of Data-Driven and Digital Medicine (D3M) at the Icahn School of Medicine at Mount Sinai, <a href=\"https:\/\/scitechdaily.com\/heartbeit-mount-sinais-ai-innovation-decoding-electrocardiograms-as-language\/\">said<\/a>: \u201cOur model consistently outperformed convolutional neural networks [CNNs], which are commonly used machine learning algorithms for computer vision tasks.\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400\">Additionally, HeartBEiT was able to highlight the specific region of the ECG associated with the abnormality.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Another author, Girish Nadkarni, MD, MPH, Director of The Charles Bronfman Institute of Personalized Medicine, said, \u201cNeural networks are considered black boxes, but our model was much more specific in highlighting the region of the ECG responsible for a diagnosis, such as a heart attack, which helps clinicians to better understand the underlying pathology.&#8221;<\/span><\/p>\n<p><span style=\"font-weight: 400\">&#8220;By comparison, the CNN explanations were vague even when they correctly identified a diagnosis.\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400\">AI\u2019s role in medical research and development is well-established, and this is another example of the innovative repurposing of machine learning (ML) models for medical applications. <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Forskare vid Mount Sinai har anv\u00e4nt maskininl\u00e4rning f\u00f6r att utveckla en ny analysmodell f\u00f6r elektrokardiogram (EKG) som kallas HeartBEiT. HeartBEiT f\u00f6rtr\u00e4nades p\u00e5 ett enormt dataset med 8,5 miljoner EKG fr\u00e5n 2,1 miljoner patienter och \u00f6vertr\u00e4ffade klassiska CNN (convolutional neural networks). EKG \u00e4r ett vanligt diagnostiskt test f\u00f6r hj\u00e4rtat och cirka 100 miljoner EKG tas varje \u00e5r bara i USA. Ett EKG m\u00e4ter den elektriska aktiviteten i hj\u00e4rtat och \u00e4r mycket informativt n\u00e4r det g\u00e4ller ett stort antal rytmst\u00f6rningar, onormal hj\u00e4rtaktivitet i samband med hj\u00e4rtinfarkt och andra former av hj\u00e4rtsjukdomar. Problemet med EKG \u00e4r att m\u00e5nga av de subtilare indikationerna<\/p>","protected":false},"author":2,"featured_media":1432,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[101],"class_list":["post-1430","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-medtech"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Researchers create high-performance AI model to detect heart disease from ECGs | DailyAI<\/title>\n<meta name=\"description\" content=\"Researchers at Mount Sinai created a unique machine learning (ML) model for diagnosing heart conditions from ECGs.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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